We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding “similar” trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%∼40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy.


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    Titel :

    Trip Prediction by Leveraging Trip Histories from Neighboring Users


    Beteiligte:


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    711829 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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